Codes
Tick Chart for MetaTrader 4
The presented indicator plots a fully-functional tick chart similar to the standard price charts, with the ability of the analysis using all the MetaTrader features
Articles
Neural Networks in Trading: Dual Time Series Clustering (DUET) for MetaTrader 5
The DUET framework offers an innovative approach to time series analysis, combining temporal and channel clustering to uncover hidden patterns in the analyzed data. This allows models to adapt to changes over time and improve forecasting quality by eliminating noise
Neural Networks in Trading: Integrating Chaos Theory into Time Series Forecasting (Final Part) for MetaTrader 5
We continue to integrate methods proposed by the authors of the Attraos framework into trading models. Let me remind you that this framework uses concepts of chaos theory to solve time series forecasting problems, interpreting them as projections of multidimensional chaotic dynamic systems
Neural Networks in Trading: Integrating Chaos Theory into Time Series Forecasting (Attraos) for MetaTrader 5
The Attraos framework integrates chaos theory into long-term time series forecasting, treating them as projections of multidimensional chaotic dynamic systems. Exploiting attractor invariance, the model uses phase space reconstruction and dynamic multi-resolution memory to preserve historical
Neural Networks in Trading: Hybrid Graph Sequence Models (Final Part) for MetaTrader 5
We continue exploring hybrid graph sequence models (GSM++), which integrate the advantages of different architectures, providing high analysis accuracy and efficient distribution of computing resources. These models effectively identify hidden patterns, reducing the impact of market noise and
Neural Networks in Trading: Hybrid Graph Sequence Models (GSM++) for MetaTrader 5
Hybrid graph sequence models (GSM++) combine the advantages of different architectures to provide high-fidelity data analysis and optimized computational costs. These models adapt effectively to dynamic market data, improving the presentation and processing of financial information
Neural Networks in Trading: Two-Dimensional Connection Space Models (Final Part) for MetaTrader 5
We continue to explore the innovative Chimera framework – a two-dimensional state-space model that uses neural network technologies to analyze multidimensional time series. This method provides high forecasting accuracy with low computational cost
Neural Networks in Trading: Two-Dimensional Connection Space Models (Chimera) for MetaTrader 5
In this article, we will explore the innovative Chimera framework: a two-dimensional state-space model that uses neural networks to analyze multivariate time series. This method offers high accuracy with low computational cost, outperforming traditional approaches and Transformer architectures
Neural Networks in Trading: Multi-Task Learning Based on the ResNeXt Model (Final Part) for MetaTrader 5
We continue exploring a multi-task learning framework based on ResNeXt, which is characterized by modularity, high computational efficiency, and the ability to identify stable patterns in data. Using a single encoder and specialized "heads" reduces the risk of model overfitting and improves the
Neural Networks in Trading: Multi-Task Learning Based on the ResNeXt Model for MetaTrader 5
A multi-task learning framework based on ResNeXt optimizes the analysis of financial data, taking into account its high dimensionality, nonlinearity, and time dependencies. The use of group convolution and specialized heads allows the model to effectively extract key features from the input data
Neural Networks in Trading: Hierarchical Dual-Tower Transformer (Final Part) for MetaTrader 5
We continue to build the Hidformer hierarchical dual-tower transformer model designed for analyzing and forecasting complex multivariate time series. In this article, we will bring the work we started earlier to its logical conclusion — we will test the model on real historical data